DSX NVCRE Senior Engineer

NVIDIA•Austin, TX

1d•$184,000 - $356,500

About The Position

NVIDIA is seeking a Senior Software Engineer to join its DSX team and build and operate tooling that verifies AI infrastructure is ready for production. The role focuses on cluster readiness and certification software, including the open-source Cluster Readiness Engine, which validates GPU clusters across Kubernetes and Slurm, measures training and inference performance, and produces trustworthy evidence for customers and partners. The engineer will develop repeatable, verifiable systems for qualifying GPU platforms and deployments. The work includes building Go and Python tooling, benchmark and workload catalogs, provenance and evidence pipelines, and validation for new GPU platforms, schedulers, and networking fabrics. The role also involves collaboration with NVIDIA teams and cloud partners to investigate performance and correctness issues, contributing to open-source projects, and authoring technical and operational documentation.

Requirements

Bachelor’s, master’s, or Ph.D. degree in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
At least 8 years of software engineering experience building infrastructure or systems software, ideally for large-scale compute, cloud, or HPC environments.
Strong Go and Python programming skills, with experience shipping production-quality services, controllers, or command-line tools.
Deep familiarity with Linux systems, containers, and Kubernetes, including writing operators or controllers against the Kubernetes API.
Working knowledge of HPC schedulers such as Slurm and GPU cluster fundamentals, including drivers, NCCL, InfiniBand or RoCE fabrics, and GPU health tooling such as DCGM.
Experience benchmarking or validating distributed systems, with a rigorous approach to measurement, reproducibility, and evidence.
Demonstrated ability to drive complex technical investigations to root cause and communicate findings clearly to engineering and leadership audiences.

Nice To Haves

Hands-on experience running or tuning large-scale distributed training or inference workloads using frameworks such as PyTorch, Megatron, NeMo, vLLM, SGLang, or TensorRT-LLM.
Experience with the NVIDIA ecosystem, including DGX systems, GB200/GB300 platforms, CUDA, NVLink and NVSwitch fabrics, and NVIDIA networking.
Prior contributions to open-source infrastructure projects, especially Kubernetes operators, scheduler plugins, or benchmarking frameworks.
Background in software supply-chain security, attestation, or signed artifacts, and in building audit-grade evidence into systems.
Familiarity with observability stacks such as Prometheus, Grafana, and OpenTelemetry; GitOps workflows; and infrastructure as code for GPU clusters.

Responsibilities

Design and build systems that make cluster validation repeatable and verifiable, shaping how NVIDIA and its partners qualify GPU platforms and deployments.
Design and implement cluster readiness and certification tooling in Go and Python for Kubernetes and Slurm, including controllers, operators, command-line tools, and scheduler integrations.
Build a catalog of benchmarks and real workloads covering communication, compute, storage, diagnostics, training, inference, reliability, and security, with architecture-specific expectations and pass/fail criteria.
Develop provenance and evidence pipelines that record what ran, where it ran, and under which configuration, and produce signed, auditable results.
Integrate new GPU platforms, schedulers, and fabrics into validation, from single-node bring-up through multi-node testing on production clusters.
Collaborate with internal platform, networking, and solutions teams and cloud partners to investigate performance and correctness gaps and feed fixes back into platforms and reference architectures.
Contribute to the open-source Cluster Readiness Engine and related projects through design documents, documentation, and reviews.
Author design documents, runbooks, and operational guides explaining cluster validation and interpretation of results.